F22 LABS - AI Engineer

What matters most

• Deploy production-grade AI systems

  The largest share of the role (65%) is designing, building and deploying agentic systems, multi-agent workflows, RAG, LLM automation and data pipelines on AWS or Gemini Enterprise / Google Cloud. Prepare to explain how an AI solution moves from design to a reliable production deployment.

• Own enterprise architecture decisions

  The posting calls for systems serving thousands of users and millions of transactions, with model, framework and architecture choices balanced against performance, cost and maintainability. Prepare a concrete example of a technical choice and its trade-offs.

• Translate client needs into measurable outcomes

  Discovery workshops, stakeholder presentations and executable technical roadmaps are part of the stated client-success work (25%). Prepare to describe how a business need becomes a solution with a measurable outcome, without assuming a particular client project.

• Use feedback to improve adoption

  The role includes working with client teams to gather usage insights and optimize solutions for adoption and impact. Prepare an example of how observed usage could change an implementation or its roadmap.

• Raise team effectiveness

  The mentoring portion (10%) covers junior-engineer guidance, code reviews, reusable accelerators and playbooks. It also calls for demonstrating AI-assisted workflows using tools such as Claude Code and Cursor while maintaining code quality.

Likely challenges to think through

• Speed versus reliability

  Likely preparation challenge, not a reported employer problem: the posting's 23x faster delivery target could create tension with its production-quality expectations. Prepare to explain how you would review, test and validate AI-assisted code rather than treating the speed target as a guarantee.

• Scale versus operating cost

  Likely preparation challenge: model and architecture choices that perform well in a small demonstration may be costly or difficult to maintain at the stated enterprise scale. Prepare to compare options using expected traffic, performance, cost and maintainability.

• Technical ambition versus client adoption

  Likely preparation challenge: a sophisticated agent or RAG design may not address the client's actual pain point or fit how teams use it. Prepare to show how discovery, a measurable outcome and usage feedback would guide what to build and revise.

Topics to prepare

• Agentic and multi-agent architecture

  Suggested revision: diagram an agent workflow for customer service, showing each agent’s responsibility, handoffs and failure paths. The posting explicitly calls for production-grade agentic systems and multi-agent workflows.

• RAG, LLM automation and data pipelines

  Suggested revision: trace how enterprise data would move through a pipeline into a RAG-based answer, and identify where incorrect or stale answers could arise. The role explicitly includes RAG architectures, LLM-powered automation and intelligent data pipelines.

• Enterprise architecture trade-offs

  Suggested revision: compare two model-and-architecture choices for a system serving thousands of users and millions of transactions, explaining performance, cost, maintainability and reliability trade-offs. These are stated decision criteria in the posting.

• Partner-platform design

  Suggested revision: sketch how an AI solution could be deployed on AWS or Gemini Enterprise / Google Cloud, marking the components whose platform choice affects cost or operations. These platforms are named in the posting; a particular service or certification is not specified.

• Client outcomes and adoption

  Suggested revision: turn a sample inventory-intelligence request into a measurable outcome, an initial technical roadmap and questions for gathering usage feedback. The posting names that project type and expects discovery, stakeholder communication and iteration with client teams.

Skills to revise

• Prototype and test an AI workflow

  Suggested exercise: build a small customer-service agent workflow or RAG prototype, then test representative requests and document failure cases. The posting emphasizes building and deploying reliable solutions, not merely making a demo work.

• Explain an architecture decision

  Suggested exercise: present a short design for an enterprise integration platform, defend its model and architecture choices, and state how success would be measured. The role involves presenting technical decisions to client stakeholders.

• Use AI-assisted development with review

  Suggested exercise: use Claude Code or Cursor, if available, to draft a small feature; review the output, correct defects and record what you verified. The posting names both tools and pairs AI-assisted development with code quality and reliability.

• Review and document reusable work

  Suggested exercise: review a sample agent-workflow change for reliability and maintainability, then write a brief reusable implementation note. The posting calls for code reviews, mentoring junior engineers, documented patterns and technical playbooks.

• Select a relevant project walkthrough

  The posting emphasizes production-grade agents, RAG, LLM automation and data pipelines. Prepare one real project example, if available, showing your role, architecture, deployment decisions, reliability work and measured outcome. Label any proposed design as hypothetical.

• Practice an enterprise-scale design exercise

  Sketch a customer-service agent or inventory-intelligence system for thousands of users and millions of transactions, as the posting describes. Be ready to explain workflow boundaries, failure handling, evaluation, monitoring and scaling trade-offs.

• Build a technical decision sheet

  The role calls for choosing models, frameworks and architectures while balancing performance, cost and maintainability. Prepare a one-page comparison for your design, including where AWS or Gemini Enterprise / Google Cloud could fit; distinguish experience you have from options you would investigate.

• Rehearse a client discovery and demo

  Prepare questions that uncover a client's pain points, constraints and success measures. Then practice a short stakeholder explanation of your proposed architecture, expected measurable outcome and what usage feedback would prompt you to change.

• Prepare evidence of engineering and team practices

  Choose concrete examples, where available, of code review, mentoring, reusable documentation and AI-assisted development with tools such as Claude Code or Cursor. Explain how you checked code quality and reliability; do not present the posting's velocity targets as your own results.

• Draft questions for F22 Labs

  Ask which client use cases are most immediate, how production performance and client impact are measured, what platform choices are already set, and how engineers collaborate with Solutions Consultants and Practice Leaders.